Spatio-temporal Crop Classification On Volumetric Data

• URL: http://arxiv.org/abs/2103.10050v1
• Date: Thu, 18 Mar 2021 07:13:53 GMT
• Title: Spatio-temporal Crop Classification On Volumetric Data
• Authors: Muhammad Usman Qadeer, Salar Saeed, Murtaza Taj and Abubakr Muhammad
• Abstract summary: Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades. Deep convolutional neural networks (DCNN) have been proposed, but they only achieved results comparable with Random Forest. In this work, we present a novel CNN based architecture for large-area crop classification.
• Score: 3.2880869992413246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest. In this work, we present a novel CNN based architecture for large-area crop classification. Our methodology combines both spatio-temporal analysis via 3D CNN as well as temporal analysis via 1D CNN. We evaluated the efficacy of our approach on Yolo and Imperial county benchmark datasets. Our combined strategy outperforms both classical as well as recent DCNN based methods in terms of classification accuracy by 2% while maintaining a minimum number of parameters and the lowest inference time.

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